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Related Concept Videos

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Design Example: Alignment of a Road Line Using GIS

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Related Experiment Videos

A YOLOv8-Based Real-Time Road Congestion Decision-Making Approach Fused with Channel-Spatial Attention and Dynamic

Wei Huang1, Heyang Xu2, Hao Bai1

  • 1Sichuan Expressway Construction and Development Group Co., Ltd., Chengdu 610041, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces an optimized YOLOv8 model for accurate vehicle detection in dense urban traffic. The enhanced model improves real-time road congestion analysis by refining feature extraction and bounding box regression.

Keywords:
UAV remote sensingYOLOv8attention mechanismtraffic congestion decision makingvehicle detection

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Traffic Engineering

Background:

  • Conventional object detection models struggle with dense urban traffic, leading to performance degradation.
  • Accurate real-time road congestion decision-making is crucial for intelligent transportation systems.

Purpose of the Study:

  • To develop an optimized YOLOv8-based detection paradigm for enhanced vehicle detection in challenging urban traffic conditions.
  • To enable accurate real-time road congestion classification through improved object detection.

Main Methods:

  • Implemented a multi-scale feature enhancement (MFE) module for high-resolution shallow feature extraction.
  • Integrated a convolutional block attention module (CBAM) into the feature fusion neck to filter noise and enhance target saliency.
  • Utilized Wise-IoU (WIoU) dynamic focusing loss to stabilize bounding box regression for dense and occluded targets.
  • Developed a quantitative congestion index (CI) model using vehicle density and average speed for real-time classification.

Main Results:

  • Achieved an mAP@0.5 of 83.1%, a 3.8% improvement over the YOLOv8 baseline.
  • Improved detection of small targets (mAP_S) by 4.3% to 23.2%.
  • Realized a real-time congestion decision accuracy of 83.8% with an inference speed of 86 FPS.

Conclusions:

  • The proposed optimized YOLOv8 model significantly enhances vehicle detection accuracy and real-time performance in dense urban traffic.
  • The MFE, CBAM, and WIoU modules independently and synergistically contribute to improved detection, particularly for small targets.
  • The model effectively supports real-time road congestion monitoring and decision-making, surpassing traditional methods.